Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems.

ABSTRACT: Uncertainty is a fundamental aspect of the natural environment, requiring the brain to infer and integrate noisy signals to guide behavior effectively. Sampling-based inference has been proposed as a mechanism for dealing with uncertainty, particularly in early sensory processing. However, it is unclear how to reconcile sampling-based methods with operational principles of higher-order brain areas, such as attractor dynamics of persistent neural representations. In this study, we present a spiking neural network model for the head-direction (HD) system that combines sampling-based inference with attractor dynamics. To achieve this, we derive the required spiking neural network dynamics and interactions to perform sampling from a large family of probability distributions-including variables

SUBMITTER: Pjanovic V 

PROVIDER: S-EPMC11888369 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature